By Battalion Chief Noel Garo and Master Firefighter Dan Fernandez
There was no single moment or conference epiphany. What started this was a series of conversations with coworkers and what kept coming up was that a lot of us had already been using AI on our own time — a personal interest, something we were experimenting with outside of work. At some point, someone started applying it to something job-related. Then someone else did. That’s how it spread.
What we began to notice, through conversations across the department, was that the problems our people were solving were increasingly administrative. Scheduling backlogs. Documentation mountains. Research that took weeks. Work that consumed time that could have been spent on training, people, on the bigger picture. Artificial intelligence, it turns out, was built for exactly this kind of friction.
The culture that made it possible
Virginia Beach Fire Department (VBFD) leadership evaluates technology on one standard: Does it make us better? The question applies to new nozzle designs, extrication tools, SCBAs, drones, everything. AI was no different.
So, when we set out to explore AI use cases, we found that the initiative was already at work, making teams better at all levels of the organization. Firefighters were experimenting at the company level, solving real problems on their own. Leadership was creating space for those experiments to grow. The fire chief, like every officer in the organization, came up as a firefighter. The problem-solving instinct does not change with rank, only the scale of the problem does.
Furthermore, the senior leadership here empowers people to go find things that make the organization better. There was clear direction from the top to seek out tools that improve how we operate.
Where AI has made the difference
The department uses a Microsoft Copilot enterprise account and is actively working to secure a Google Gemini enterprise account to expand those capabilities. Across the organization, AI is being used to draft correspondence, summarize meeting notes and accelerate operational communications. The following are some of the more significant examples of where that impact has been felt:
Document and manual creation. The Training Division built multiple instructor manuals using AI, covering critical areas of fire service training. The time saved in document creation has been significant, freeing personnel to focus on delivery rather than production.
Scheduling. Building the in-service training schedule is a complex coordination problem — AM and PM rotations, unit cohesion, station coverage, competing constraints across the department. AI managed all of it simultaneously. After the schedule was released, the feedback from the field was that this year’s in-service felt like the right amount of time — not too long, not rushed — compared to previous years.
Professional documentation. The recruit academy’s instructional staff uses AI to produce consistent, standardized documentation such as performance notes, evaluations and administrative records. The instructor describes the situation in plain language, attaches the relevant SOP, and AI produces a draft at the appropriate reading level. The instructor reviews, edits and signs off. The result: consistent quality across the organization and reduced documentation liabilities.
Data analysis and cost modeling. The VBFD leveraged AI to model lift assist call volume against Virginia Beach’s aging population data, producing a cost-benefit analysis that identified meaningful financial savings from altering the response model. These are budget arguments that previously took months to construct. With AI, they take hours.
Operational training innovation. VBFD personnel developed two operational training tools — a command simulation platform and a hydraulic pump trainer both accessible through a website that provide firefighters and officers with realistic, on-demand training without removing apparatus from service or requiring the multitude of personnel a traditional command simulation demands. The tools were built through iterative AI-assisted development that required significant technical problem-solving and a working knowledge of systems architecture. This took a lot of work, but it reflects what becomes possible when organizations invest in personnel who operate at an AI technician level, the same way departments cultivate hazmat and rope technicians: Some personnel work at awareness, some at operations, and a skilled few can build capable tools that help improve an organization at a fraction of the cost.
Overcoming resistance
The two questions we hear most focus on security and over-reliance.
On security, that’s exactly why enterprise accounts matter. A free tool learns from your inputs. An enterprise account keeps your department’s information inside a protected environment and meets compliance standards IT departments may require.
On over-reliance, that’s a fair concern, and the answer is that the human stays responsible. AI drafts, suggests and analyzes. The person signing off on the work is still accountable for its accuracy. The tool doesn’t replace that judgment; it just reduces the time it takes to get there.
Any tool in the station can cause harm if you do not know how to use it properly. That is not an argument against the tool; it’s an argument for training. AI is no different. Our plan is straightforward: Secure additional enterprise accounts, invest in people, find those already engaged, bring them together, and build from shared experience. The same way any specialty team gets started.
What we would tell other departments
Keep the human in the loop. AI doesn’t create better leaders; it speeds up the middle. The beginning and the end still require the people who know the organization and the people in it. Review everything before it goes out.
Pick one problem that is stealing time from your people. Open a tool. Ask it for help. The barrier is lower than most people assume, and the return comes faster than most people expect. The way we see it, AI improves efficiency without sacrificing quality. When the administrative load gets lighter, the bigger work gets done.
ABOUT THE AUTHORS
Noel Garo is a battalion chief with the Virginia Beach (Va.) Fire Department, assigned to operations. He has 20 years of fire service experience between the Virginia Beach and Chesapeake fire departments. Garo holds an associate in fire science and a bachelor’s degree in occupational health and safety, both from Columbia Southern University. He is a CFAI Level 2 Peer Assessor and currently serves as one of the department’s Accreditation team members. Garo serves as the Vice President of IAFF Local 2924.
Dan Fernandez is a master firefighter with the Virginia Beach (Va.) Fire Department with 10 years of fire service experience currently assigned to operations. He holds a bachelor’s degree from James Madison University and an MBA from the University of Maryland University College. Fernandez is the founder of Ask Lucy, an AI consultancy dedicated to empowering small businesses and public safety agencies with practical, scalable AI and automation solutions.
